
We propose a novel plug-in network that can be applied to various models. This network integrates novel background segmentation and feature fusion techniques, which can effectively improve the accuracy of fine-grained visual classification.

The proposed network outperforms state-of-the-art networks and improves the accuracy to 92.77% (+0.97%) and 92.83% (+1.83%) on CUB200-2011[1]} and NABirds[2]}, respectively.

